Experience With the Ibm Zisc036 Neural Network Chip

Lindsey, Clark S.; Lindblad, Thomas; Sekhniaidze, G.; Székely, G.; Minerskjöld, Maxim

doi:10.1142/s0129183195000460

Cited by 6 publications

(2 citation statements)

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“…The first is -a general, but probably costly, system that can be re-programmed for many kinds of tasks -such as Adaptive Solutions CNAPS [19]. The second is -a specialized, but relatively cheaper, chip that does single task quickly and efficiently, such as IBM ZISC [20].…”

Section: Hardware Vs Software Implementation Of Annsmentioning

confidence: 99%

Hardware Design for Machine Learning

Jawandhiya¹

2018

IJAIA

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Section: Hardware Vs Software Implementation Of Annsmentioning

confidence: 99%

Hardware Design for Machine Learning

Jawandhiya¹

2018

IJAIA

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“…• Lindblad et al [43] used the ZISC to look for Higg's boson events amongst the very large number of traces made by the elementary particles created in high energy particle accelerators. Lindsey et al [45], and Minerskjöld [45] also report tests of the ZISC used in high energy physics tasks. • Madani et al [49] used the ZISC to devise a new approach to control that implements a parallel real-time intelligent adaptive controller.…”

Section: The Zisc and The Cognimemmentioning

confidence: 99%

Infrared target-flare discrimination using a ZISC hardware neural network

Labonté

Deck

2009

J Real-Time Image Proc

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The last generation of infrared imaging aircraft seekers and trackers uses pattern recognition algorithms to find and keep a lock on an aircraft in the presence of decoy flares. These algorithms identify targets, based on the features of the various objects in the missile's field of view. Because modern both aircrafts and missiles fly faster than sound, speed of operation of the target identifier is critical. In this article, we propose a target recognition system that respects this time constraint. It is based on an artificial neural network implemented in hardware, as a set of parallel processors on a commercially available silicon chip called a ZISC, for zero instruction set computer. This chip would be integrated in the infrared missile seeker and tracker. We describe the characteristics of the images that the image processing module of this seeker and tracker extracts from the infrared video frames and show how to construct from these translation and rotation invariant features that can be used as input to the neural network. We determine the individual discriminating power of these features by constructing their histograms, which allows us to eliminate some as not being useful for our purpose. Finally, by testing our system on real data, we show that it has a 90% success rate in aircraft-flare identification, and a processing time that during this time, the aircrafts and missiles will have traveled only a few millimeters. Most of the images on which the neural network makes its mistakes are seen to be hard to recognize even by a human expert.

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Neuromorphic Learning VLSI Systems: A Survey

Cauwenberghs¹

The Springer International Series in Engineering and Computer Science

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INTRODUCTIONCarver Mead introduced "neuromorphic engineering" [1] as an interdisciplinary approach to the design of biologically inspired neural information processing systems, whereby neurophysiological models of perception and information processing in biological systems are mapped onto analog VLSI systems that not only emulate their functions but also resemble their structure [18]. The motivation for emulating neural function and structure in analog VLSI is the realization that challenging tasks of perception, classification, association and control successfully performed by living organisms can only be accomplished in artificial systems by using an implementation medium that matches their structure and organization.Essential to neuromorphic systems are mechanisms of adaptation and learning, modeled after the "plasticity" of synapses and neural structure in biological systems [25,4]. Learning can be broadly defined as a special case of adaptation whereby past experience is used effectively in readjusting the system response to previously unseen, although similar, stimuli. Based on the nature and availability of a training feedback signal, learning algorithms for artificial neural networks fall under three broad categories: unsupervised, supervised and reward/punishment (reinforcement). Physiological experiments have revealed plasticity mechanisms in biology that correspond to Hebbian unsupervised learning [19], and classical (pavlovian) conditioning [17,22] characteristic of reinforcement learning.

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Experience With the Ibm Zisc036 Neural Network Chip

Cited by 6 publications

References 0 publications

Hardware Design for Machine Learning

Hardware Design for Machine Learning

Infrared target-flare discrimination using a ZISC hardware neural network

Neuromorphic Learning VLSI Systems: A Survey

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